Patients with end-stage renal disease who will soon start renal- replacement therapy must decide among three treatment modalities: hemodialysis, peritoneal dialysis, and renal transplantation. Most studies comparing these modalities have focused on duration of a single spell -- from start of treatment until death. Decisions about the preferred modality also may be influenced by estimates of the probability of entry to and exit from the various treatment modalities and how initial renal- replacement treatment choices affect duration until first renal transplantation. The goals of this study are to (1) develop and test a flexible statistical framework for analyzing longitudinal data from the U.S. Renal Data System and to (2) better describe the time spent and the pattern of transitions between hemodialysis and peritoneal dialysis until first renal transplantation or death. A generalized variant of a transition-probability model, expanded beyond applications using Markov- chain models, underlies all analyses. The model incorporates two basic elements: duration distributions and probabilities of entry into one of five health-treatment statuses -- no treatment, hemodialysis, peritoneal dialysis, renal transplantation, and death. The model allows for time- varying covariates and hazard rates, and adjustments for left censoring, where the statistician observes spells that are in the middle of continuation. MonteCarlo simulation will be used to characterize the distribution of health-treatment activities until renal transplantation or death and to determine how these activities vary according to patient demographic and health characteristics. Study findings will provide better patient-specific estimates of the prospects of modality decisions for end- stage renal disease. The study also will provide an improved framework for evaluating longitudinal, observational data from clinical databases.